Regression kriging to improve basal area and growing stock volume estimation based on remotely sensed data, terrain indices and forest inventory of black pine forests

Abstract

Background: The use of satellite imagery to quantify forest metrics has become popular because of the high costs associated with the collection of data in the field.
Methods: Multiple linear regression (MLR) and regression kriging (RK) techniques were used for the spatial interpolation of basal area (G) and growing stock volume (GSV) based on Landsat 8 and Sentinel-2. The performance of the models was tested using the repeated k-fold cross-validation method.
Results: The prediction accuracy of G and GSV was strongly related to forest vegetation structure and spatial dependency. The nugget value of semivariograms suggested a moderately spatial dependence for both variables (nugget/sill ratio approx. 70%). Landsat 8 and Sentinel-2 based RK explained approximately 52% of the total variance in G and GSV. Root-mean-square errors were 7.84 m² ha^-1 and 49.68 m³ ha^-1 for G and GSV, respectively.
Conclusions: The diversity of stand structure particularly at the poorer sites was considered the principal factor decreasing the prediction quality of G and GSV by RK.